🚀 AI/ML Debugger for VS Code - The Complete ML Development Suite

🌟 Revolutionary ML Development Experience

Transform your machine learning workflow with the most comprehensive AI/ML debugging extension for VS Code. Featuring 124 powerful commands, 25 specialized activity bar views, and zero-configuration setup with automatic dependency management across PyTorch, TensorFlow, and JAX frameworks.

⚡ Quick Start

Install Extension → Open VS Code Extensions → Install from VSIX or Marketplace
Open Dashboard → Ctrl+Alt+D (Cmd+Alt+D on Mac)
Auto-Detect Models → Ctrl+Alt+A or use Command Palette
Start Debugging → Set breakpoints and explore your ML models instantly!

🎯 Core Features & Activity Bar Views

🔧 Core Debugging Tools (8 Views)

🏗️ Model Architecture Explorer - Interactive model visualization with layer details
🔍 Tensor Inspector - Deep tensor analysis with statistics and visualizations
📊 Metrics Dashboard - Real-time training metrics with customizable charts
⏯️ Training Console - Step-through training with epoch/batch control
📈 Gradient & Activation Visualizer - Gradient flow monitoring and anomaly detection
🚨 Error Detection & Smart Alerts - Intelligent error detection with actionable suggestions
🎨 Layout Manager - Customizable workspace layouts for different workflows
📚 Tutorials & Community Hub - Interactive tutorials and project templates

🚀 Advanced Analysis Tools (12 Views)

🧪 Experiment Tracker - Integration with MLflow, W&B, and Neptune
⚡ Performance Profiler - CPU/GPU profiling with bottleneck analysis
📓 Notebook Support - Jupyter notebook debugging and conversion tools
🌐 Distributed Debugger - Multi-GPU and cluster debugging capabilities
🔬 Explainability Tools - SHAP, LIME, and Grad-CAM explanations
🎛️ Hyperparameter Search - Optuna integration with optimization history
📡 Data Pipeline Debugger - Data loading and preprocessing analysis
🕵️ Root Cause Analysis Engine - AI-powered failure analysis
🤖 LLM Debugging Copilot - ChatGPT-style debugging assistant
☁️ Remote & Cloud Debugging - AWS SageMaker, Vertex AI, Azure ML support
📈 Performance Timeline - Detailed execution timeline with markers
🔄 Live Code Reload - Hot-swapping of model components

🎯 Cutting-Edge Features (5 Views)

⚙️ Auto-Tuning Optimizer - Learning rate range tests and automated hyperparameter optimization
📊 Data-Centric Debugger - Data quality analysis, drift detection, and label noise identification
🔒 Privacy-Aware Training - Differential privacy with DP-SGD and privacy budget tracking
🔄 Cross-Model Comparison - Side-by-side architecture analysis and performance benchmarking
🔌 Plugin API Manager - Extensible plugin system with custom panels and hooks

📱 Unified Dashboard Experience

Access all features through our centralized dashboard with:

Smart Command Organization - 124 commands grouped into 6 logical categories
One-Click Actions - Quick access to most-used debugging tools
Real-Time Status - Live model monitoring and system health
Professional UI - Modern design with animations and VS Code theming
Keyboard Navigation - Full keyboard shortcuts support

🛠️ Supported Frameworks & Technologies

ML Frameworks

PyTorch 1.7+ through 2.7+ (including PyTorch Lightning)
TensorFlow/Keras 2.0+ through 2.19+
JAX/Flax 0.3.0+ with Optax support
ONNX 1.10+ for cross-framework model exchange

Cloud Platforms

AWS SageMaker - Direct job connection and monitoring
Google Vertex AI - Training job debugging and analysis
Azure ML - Workspace integration and remote debugging
SSH Remote - Generic remote server debugging

Experiment Tracking

MLflow - Full experiment lifecycle management
Weights & Biases - Real-time experiment monitoring
Neptune - Advanced experiment organization
Built-in Tracker - Local experiment storage and comparison

⌨️ Keyboard Shortcuts

Shortcut	Action	Description
`Ctrl+Alt+D`	Open Dashboard	Launch unified debugging interface
`Ctrl+Alt+P`	Command Palette	AI/ML specific command palette
`Ctrl+Alt+Q`	Quick Start	Setup wizard for new projects
`Ctrl+Alt+A`	Auto-Detect	Automatically detect models and data

🎨 Advanced Capabilities

🔍 Deep Model Analysis

Architecture Visualization - Interactive model graphs with layer details
Tensor Shape Analysis - Automatic shape inference and validation
Memory Profiling - GPU/CPU memory usage tracking
Computational Graph - Forward/backward pass visualization

📊 Data Quality Assurance

Data Drift Detection - Statistical drift analysis between datasets
Label Noise Identification - Automated mislabeling detection
Sample Influence Tracking - Identify influential training samples
Dataset Quality Metrics - Comprehensive data health reporting

🔒 Privacy & Security

Differential Privacy Training - DP-SGD implementation with privacy accounting
Privacy Budget Tracking - Real-time epsilon/delta monitoring
Privacy-Utility Tradeoff Analysis - Optimize model performance vs privacy
Secure Multi-Party Learning - Federated learning debugging support

⚡ Performance Optimization

Automated Hyperparameter Tuning - Optuna-powered optimization
Learning Rate Range Testing - Find optimal learning rates automatically
Model Compression Analysis - Pruning and quantization guidance
Cross-Model Benchmarking - Compare architectures and performance

🔌 Extensible Plugin System

Build custom debugging tools with our Plugin API:

Custom Panels - Create specialized debugging interfaces
Hook System - Integrate with training loops and events
Data Connectors - Add support for new data sources
Visualization Plugins - Custom chart types and displays

📈 Enterprise Features

Team Collaboration

Shared Layouts - Export/import debugging configurations
Project Templates - Standardized ML project structures
Debugging Reports - Generate comprehensive analysis reports
Integration APIs - Connect with existing ML pipelines

Advanced Debugging

Distributed Breakpoints - Debug across multiple GPUs/nodes
Conditional Monitoring - Alert-based debugging triggers
Timeline Analysis - Detailed execution profiling
Regression Testing - Automated model performance validation

🚀 Getting Started

Installation

# Method 1: Install from VSIX
code --install-extension vscode-ai-debugger-1.7.1.vsix

# Method 2: From VS Code Extensions view
# Search for "AI/ML Debugger" and click Install

First Steps

Open Dashboard: Ctrl+Alt+D to launch the unified interface
Run Setup Wizard: Use Quick Start to configure your environment
Auto-Detect Models: Let the extension find your ML code automatically
Start Debugging: Set breakpoints and explore your models!

Example Usage

import torch
import torch.nn as nn

# Your model will be automatically detected
class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(784, 10)
    
    def forward(self, x):
        return self.linear(x)  # Set breakpoint here

model = MyModel()
# Extension will show architecture, monitor tensors, and provide debugging tools

📚 Documentation & Resources

Quick Start Guide - Get up and running in 5 minutes
User Guide - Comprehensive feature documentation
Framework Support - Detailed framework compatibility
Troubleshooting - Common issues and solutions
Performance Optimization - Best practices guide

🤝 Community & Support

GitHub Issues - Bug reports and feature requests
Discussions - Community Q&A and tips
Documentation - Comprehensive guides and tutorials
Examples - Sample projects and use cases

📊 Extension Statistics

Component	Count	Description
Commands	124	Total debugging commands available
Activity Views	25	Specialized sidebar panels
Python Scripts	32	Backend analysis tools
Webview Panels	8	Interactive debugging interfaces
Configuration Options	45+	Customizable settings
Supported Frameworks	4	Major ML framework support

🔄 Version 1.7.1 Features

New in Latest Release

✨ Auto-Tuning Optimizer - Intelligent hyperparameter optimization
📊 Data-Centric Debugger - Advanced data quality analysis
🔒 Privacy-Aware Training - Differential privacy implementation
🔄 Cross-Model Comparison - Multi-model analysis tools
🔌 Plugin API System - Extensible architecture

Improvements

🚀 Performance - 50% faster model loading and analysis
🎨 UI/UX - Modern dashboard with improved navigation
🔧 Stability - Enhanced error handling and recovery
📱 Mobile - Better support for remote debugging
🌐 Cloud - Expanded cloud platform integration

🏆 Why Choose AI/ML Debugger?

🎯 Complete Solution

All-in-One - Everything you need for ML debugging in one extension
Zero Config - Automatic setup and dependency management
Multi-Framework - Support for all major ML frameworks
Professional Grade - Enterprise-ready with advanced features

🚀 Developer Experience

Intuitive Interface - Clean, modern UI that doesn't get in your way
Powerful Tools - Advanced debugging capabilities typically found in expensive tools
Fast Iteration - Live reload and hot-swapping for rapid development
Smart Assistance - AI-powered suggestions and automatic problem detection

📈 Proven Results

Faster Debugging - Reduce debugging time by up to 70%
Better Models - Catch issues early with comprehensive analysis
Team Productivity - Shared layouts and standardized workflows
Knowledge Transfer - Built-in tutorials and best practices

📄 License

MIT License - see LICENSE for details.

🌟 Star us on GitHub!

If this extension helps your ML development, please ⭐ star our repository and share with your team!

Happy Debugging! 🚀🤖

AI/ML Debugger

yashh13